Rates of change of adaptive variation in Picea mariana visualized by GIS using a differntial systematic coefficient

Journal Article

Development

Data from a short-term provenance trial of black spruce (Picea mariana (Mill.) B.S.P.) were used to illustrate a methodology to help locate breeding zone boundaries using adaptive variation models. Four height growth and survival variables were summarized by principal components analysis (PCA), and the first three axis scores were regressed against climate data determined from a recently developed Ontario Climate Model. The regression equations were used to model the PCA axes, and these models were interpreted as the three main components of adaptive variation in the data. These models were converted to geographic grids using GIS software. In a manner similar to that proposed for differential systematics applications, the Differential Systematic Coefficient (DSC) was adapted to be an indicator of the weighted average rate of change of clinally expressed adaptive variation over distance. An output grid was determined based on the DSC values, such that grid cells with higher coefficient values were made to appear darker on the resultant map; thus, the shaded areas corresponded to steeper portions of the clines of adaptive variation and serve as desirable indicators of the best locations for breeding zone boundaries.